1. Technical Field
The present invention relates to a method for providing generic formatted data to a digital data processing means.
2. Background Art
When new algorithms are to be developed for one or more object processing platforms with major restrictions in terms of memory consumed and computational power consumed, the development process takes a very long time. Indeed, each platform has its own characteristics, both in terms of equipment (for example, the number and type of processors or size and type of memory) and in terms of language used (C for a scalar processor and assembly language for a vector signal processor). Thus, even if an algorithm has already been developed for a platform, its optimised adaptation for another platform requires that the program or code be rewritten virtually in full.
For example, a signal processor has a small local memory and must copy data from and to the main memory from this local memory. For another example, a vector signal processor, notably of SIMD (Single Instruction Multiple Data) type must process data grouped into vectors of different sizes according to platform and the vectors cannot be expressed independently from the platform when a language such as language C is used.
These adaptations constitute an obstacle in the distribution of new algorithms since, for example, the adaptation of an image processing software program to a type of photographic equipment can take several months to develop.
The market for cameras, notably telephones fitted with a camera, of the “cameraphone” type, is very dynamic, with renewal of the range every six to twelve months and a very fast evolution in terms of functions. Therefore, being able to adapt fast to these new functions has become essential in order to keep up with the market.
Furthermore, computational power is increasing very fast and it is possible therefore to take advantage of this performance with increasingly powerful algorithms, provided that these can be optimised quickly. Furthermore, the characteristics of the sensors are evolving very fast and it is therefore necessary to adapt the image processing algorithms to the characteristics of these sensors, notably noise-related characteristics which increase with miniaturization, provided that these can be optimised quickly.